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Hello.
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My name is Martin Kronberg, and this is the IoT Developer Show
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season two.
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During our break, we've been busy reworking the show,
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so think of this less like a sequel and more
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like the gritty reboot.
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We'll be coming out with a new show
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every other Wednesday for the rest of the season.
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Moving forward, the IoT Dev Show is going
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to have an all new format.
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We're going to be taking deep dive looks into specific IoT
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technologies over the course of multiple episodes grouped
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into a series.
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Last season, I gave you guys a broad overview of all the
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cool Intel IoT tech with some special guests.
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And this season I'll be up here, a one man show,
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leading you through deeper dives into the technology, the tools
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available for developers, and demos that have
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been built using those tools.
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For the first series of episodes,
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we're taking a look at Open Visual Inference and Neural
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Network Optimization Toolkit, or more
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simply, the OpenVINO Toolkit, which gives developers
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the power to create cutting edge AI powered computer vision
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applications.
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Intel computer vision technologies
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have grown over the last year and have
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combined with Intel's Deep Learning Toolkit
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to form OpenVINO.
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But before we get to the details of OpenVINO,
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let me show you guys a cool demo.
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Here is the head position and emotional state detector demo.
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It's running on a brand new IEI Tank,
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which is a coupe piece of hardware
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that we're going to be covering later on.
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I'm using a couple of deep neural network models
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to detect the position and orientation
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of my face, an analysis of my gender,
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my age, and even my mood.
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All this is running at the edge on the tank
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and running at over 120 frames per second.
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And that's what OpenVINO's all about--
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leveraging powerful neural network processing of video
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as fast as possible on Intel architecture.
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Want to learn more about how this demo works
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and how you can build something like this yourself?
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Well, stay tuned, because we're going to cover
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all of that and much more.
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First of all, let's do a quick overview
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of traditional computer vision versus deep learning.
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In traditional computer vision, an image
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is analyzed using programmatic methods.
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For instance, if we're looking to identify a face,
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one method uses Haar cascade classifiers.
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This method relies on taking the difference of pixel values
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in various areas and linking it to known features,
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such as edges, eyes, so on.
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We can then say that two eyes and an oval is a face.
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In deep neural networks, this approach
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is radically different.
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Instead of telling the computer of what features to look for--
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eyes and so on--
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we show the computer 10,000 images
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of a face from various angles, and then it
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learns what it looks like by adjusting
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the structure of a complex, interconnected
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network of nodes.
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If this sounds like a black box to you, you wouldn't be alone.
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In an article from the MIT Technology Review
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called The Dark Secret at the Heart of AI,
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AI engineer Joel Dudley said, "We can build these models,
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but we don't know how they work."
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But the fact of the matter is that they do work and work
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extremely well.
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In fact, with purpose built deep learning models,
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a computer can recognize objects faster and more accurately
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than any human.
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But for now, what we need to know
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is that deep learning has two components-- a training phase,
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where the computer learns to identify objects,
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and an inference phase, where the now trained
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model is used to infer the identity of unknown objects.
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Now, with that out of the way, let's take a look
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at what's inside OpenVINO.
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It's a combination of tools for computer vision and AI.
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It uses OpenCV 3.3, which has been optimized
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for Intel architecture.
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OpenCV can be used for pre-processing
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an image for analysis and then running analysis
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on it, either through the traditional programmatic
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methods or deep neural networks.
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OpenVINO also has a custom inference engine built by Intel
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for running deep neural networks for computer vision.
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And inference engine is what's used
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to run the inference phase of deep learning
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that I mentioned earlier.
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What makes this inference engine awesome
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is its flexibility and its performance.
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It's made to utilize both your Intel
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CPU, your integrated Intel GPU, as well as a VPU,
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like the Movidius Compute Stick, or an FPGA,
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like the Altera Arria 10.
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It's also been optimized to use the latest and fastest APIs
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to access all of those processors.
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Using various processors for a single task
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is called heterogeneous computing,
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and it's part of what makes OpenVINO so fast.
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So how can you start developing using this toolkit?
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Well, we have a ton of documentation out
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there on IDZ and a few GitHub pages to get you started.
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We also have two developer kits--
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the UP Squared AI vision Development
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Kit that can be used for rapid prototyping,
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and the IEI Tank, which can be used
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for more demanding applications in an industrial environment.
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They both come loaded with all the software
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alongside awesome hardware to help you
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get started developing fast.
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That's all the time we have for today.
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In the next four episodes, we're going
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to cover all we saw today in more detail.
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I'm going to show you more awesome demos,
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talk about all the neural net models available,
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the IDEs that you can use, and deep dive
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into some of the reference designs.
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We're also going look at the hardware
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and talk about heterogeneous computing.
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Thanks for watching, and we'll see you guys in two weeks.
